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New ICESat-2 satellite LiDAR data allow first global lowland DTM suitable for accurate coastal flood risk assessment
No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km2 of land below 10, 5, and 2 m +MSL. The 0.93 million km2 of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available.
Estimating dissolved carbon concentrations in global soils : a global database and model
Dissolved carbon (C) leaching in and from soils plays an important role in C transport along the terrestrial-aquatic continuum. However, a global overview and analysis of dissolved carbon in soil solutions, covering a wide range of vegetation types and climates, is lacking. We compiled a global database on annual average dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) in soil solutions, including potential governing factors, with 762 entries from 351 different sites covering a range of climate zones, land cover types and soil classes. Using this database we develop regression models to calculate topsoil concentrations, and concentrations versus depth in the subsoil at the global scale. For DIC, the lack of a proportional globally distributed cover inhibits analysis on a global scale. For DOC, annual average concentrations range from 1.7 to 88.3 (median = 25.27) mg C/L for topsoils (n = 255) and from 0.42 to 372.1 (median = 5.50) mg C/L for subsoils (n = 285, excluding lab incubations). Highest topsoil values occur in forests of cooler, humid zones. In topsoils, multiple regression showed that precipitation is the most significant factor. Our global topsoil DOC model ( R2 = 0.36 ) uses precipitation, soil class, climate zone and land cover type as model factors. Our global subsoil model describes DOC concentrations vs. depth for different USDA soil classes (overall ( R2 = 0.45 ). Highest subsoil DOC concentrations are calculated for Histosols.
WaterSNIP : een nieuwe manier om de waterkwaliteit te monitoren
Het Water Sensoren Nutriënten Innovatie Programma (WaterSNIP) van het Landelijk Meetnet effecten Mestbeleid (LMM) richt zich op de vraag of de inzet van nieuwe technologie, zoals waterkwaliteitssensoren, zal leiden tot een efficiënter meetnet. Daarnaast heeft het programma tot doel om de schaal te verkleinen waarover uitspraken over de waterkwaliteit kunnen worden gedaan.
A Bayesian hindcasting method of levee failures applied to the Breitenhagen slope failure
Hindcasting of past levee failures enhances insights in the performance and vulnerability of levees. The scarcity of field evidence makes identifying the cause(s) of failure difficult. Under these circumstances, multiple scenarios and model choices are possible to characterise and to model the failure. This paper shows how probabilistic Bayesian techniques advance the procedure of hindcasting of levee failures. In the developed approach, a-priori levee information, and failure observations are systematically taken into account to determine the most likely scenario and the most representative model choices to characterise the failure most accurately. Observations, such as the slip surface, are taken into account in the probability estimates. The levee failure near Breitenhagen, Germany (2013) is used as a case study. The levee failed during river floods due the instability of the landside slope. The levee failure was most likely triggered by locally weak soil conditions and unexpected high water pressures due a connection between a pond on the riverside of the levee and the aquifer. These conditions were likely caused by the occurrence of a previous breach at this location. The approach developed in this paper is expected to support a more systematic and objective method of analysis of other levee failures.
The Virtual River Game : gaming using models to collaboratively explore river management complexity
Serious games are increasingly used as tools to facilitate stakeholder participation and stimulate social learning in environmental management. We present the Virtual River Game that aims to support stakeholders in collaboratively exploring the complexity of a changed river management paradigm in the Netherlands. The game uses a novel, hybrid interface design that features a bidirectional coupling of a physical game board to computer models. We ran five game sessions involving both domain experts and non-experts to assess the game’s value as a participatory tool. The results show that the game was effective in enabling participants to collaboratively experiment with various river interventions and in stimulating social learning. As a participatory tool, the game appears to be valuable to introduce non-expert stakeholders to Dutch river management. We further discuss how the hybrid interface combines qualities usually found in board and computer games that are beneficial in engaging stakeholders and stimulating learning.
An engineering approach to study the effect of saturation-dependent capillary diffusion on radial Buckley-Leverett flow
1D water oil displacement in porous media is usually described by the Buckley-Leverett equation or the Rapoport-Leas equation when capillary diffusion is included. The rectilinear geometry is not representative for near well oil displacement problems. It is therefore of interest to describe the radially symmetric Buckley-Leverett or Rapoport-Leas equation in cylindrical geometry (radial Buckley-Leverett problem). We can show that under appropriate conditions, one can apply a similarity transformation (r, t) > n = r2/(2t) that reduces the PDE in radial geometry to an ODE, even when capillary diffusion is included (as opposed to the situation in the rectilinear geometry. We consider two cases (1) where the capillary diffusion is independent of the saturation and (2) where the capillary diffusion is dependent on the saturation. It turns out that the solution with a constant capillary diffusion coefficient is fundamentally different from the solution with saturation-dependent capillary diffusion. Our analytical approach allows us to observe the following conspicuous difference in the behavior of the dispersed front,where we obtain a smoothly dispersed front in the constant diffusion case and a power-law behavior around the front for a saturation-dependent capillary diffusion. We compare the numerical solution of the initial value problem for the case of saturation-dependent capillary diffusion obtained with a finite element software package to a partially analytical solution of the problem in terms of the similarity variable n.
Morfologie en ecologie van de Scheldemonding : overzicht van bestaande kennis en data
Rijkswaterstaat is voornemens om een suppletie in de monding van het Schelde-estuarium aan te leggen, om te onderzoeken of suppleties in dit gebied bijdragen aan de instandhouding van het kustfundament en de veiligheid van de kust en het achterland. De data die worden verkregen middels de monitoring van deze suppletie zullen ook worden ingezet om de morfologische en ecologische kennis van het gebied te vergroten. Dit rapport beschrijft de bestaande kennis en data van de morfologie en ecologie van de Schelde-monding. Het heeft mede tot doel hiaten in de morfologisch en ecologische kennis en monitoringsdata te definiëren.
The potential of nature-based flood defences to leverage public investment in coastal adaptation : cases from the Netherlands, Indonesia and Georgia
Nature-based flood defences (NBFD) are receiving considerable attention in the coastal adaptation field. Advocates of NBFD point to their cost-effectiveness, flexibility and the range of co-benefits they produce beside flood risk reduction. However, NBFD are not yet common practice. One reason for this may be found in financial barriers. To date, there has been little attention for financial aspects of NBFD, as the literature has focused on design, effectiveness and socio-economic impact of such projects. We address this gap by analysing the financial attractiveness of real-world NBFD from the perspective of the public actor. We address the following research questions: through which mechanisms can public investments in NBFD projects be leveraged? ; and what are the enabling conditions for these mechanisms? We find two types of revenue generating mechanisms: value capture, in which the public actor generates revenues from private beneficiaries through taxes; and co-investment, in which the project attracts in-kind or cash contributions from other actors. We illustrate the potential of these leveraging mechanisms in four case studies and find that NBFD can generate significant tax revenues in locations with high demand for certain co-benefits, whereas project size, type, timing and beneficiaries of co-benefits determine the potential for co-investment.
Improved transferability of data-driven damage models through sample selection bias correction
Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.
HESS opinions: Improving the evaluation of groundwater representation in continental to global scale models
Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system, driven by crucial Earth science and sustainability problems. These models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. A key question for this nascent and rapidly developing field is how to evaluate the realism and performance of such large-scale groundwater models given limitations in data availability and commensurability. Our objective is to provide clear recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We identify three evaluation approaches, including comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); comparing several models with each other with or without reference to actual observations (model-based evaluation); and comparing model behavior with expert expectations of hydrologic behaviors that we expect to see in particular regions or at particular times (expert-based evaluation). Based on current and evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches may significantly improve the realism of groundwater representation in large-scale models, and thus our quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on these challenges, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.